Skip to main content

Exploiting Views for Collaborative Research Data Management of Structured Data

  • Conference paper
  • First Online:
From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries (ICADL 2022)

Abstract

Data-driven analysis plays a vital role in research projects, and sharing data with collaborators inside or outside a project is supposed to be daily scientific work. There are various tools for research data management, which offer features like storing data, meta-data indexing, and provide options to share data. However, currently, none of them offers capabilities for sharing data in different levels of detail without excessive data duplication. Naturally, sharing data by duplication is a tedious process, as preparing data for sharing typically involves changing temporal resolution (i.e., aggregation) or anonymization, e.g., to ensure privacy. In this paper, instead of re-inventing the wheel, we ask whether the concept of views, a well-established concept in relational databases, fulfills the above requirement. Conducting a case study for a project employing sharing of learning analytics data, we propose a framework that allows for fine-granular configuration of shared content based on the concept of views. In the case study, we a) analyze a data reuse scenario based on the FAIR principles, b) suggest a concept for using views for data sharing, and c) demonstrate its feasibility with a proof-of-concept.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://dsgvo-gesetz.de/.

  2. 2.

    https://www.re3data.org/.

  3. 3.

    https://v2.sherpa.ac.uk/opendoar/.

  4. 4.

    https://creativecommons.org/.

  5. 5.

    https://pkp.sfu.ca/ojs/.

References

  1. Amorim, R.C., Castro, J.A., Rocha da Silva, J., Ribeiro, C.: A comparison of research data management platforms: architecture, flexible metadata and interoperability. Univ. Access Inf. Soc. 16(4), 851–862 (2016). https://doi.org/10.1007/s10209-016-0475-y

    Article  Google Scholar 

  2. Bloemers, M., Montesanti, A.: The FAIR funding model: providing a framework for research funders to drive the transition toward FAIR data management and stewardship practices. Data Intell. 2(1–2), 171–180 (2020). https://doi.org/10.1162/dint_a_00039

    Article  Google Scholar 

  3. Codd, E.F.: A relational model of data for large shared data banks. Commun. ACM 26(1), 64–69 (1983). https://doi.org/10.1145/357980.358007

    Article  Google Scholar 

  4. Devarakonda, R., Palanisamy, G., Green, J., Wilson, B.: Data sharing and retrieval using OAI-PMH. Earth Sci. Inf. 4(1), 1–5 (2011). https://doi.org/10.1007/s12145-010-0073-0

    Article  Google Scholar 

  5. Dewey, M.: Dewey decimal classification and relative index. 2, Schedules 000-599. OCLC Library (1989)

    Google Scholar 

  6. Dietrich, A.: Liascript: a domain-specific-language for interactive online courses. In: Multi Conference on Computer Science and Information Systems, p. 186 (2019)

    Google Scholar 

  7. Drachsler, H., Greller, W.: Privacy and analytics: it’s a delicate issue a checklist for trusted learning analytics. In: Gašević, D., Lynch, G., Dawson, S., Drachsler, H., Penstein Rosé, C. (eds.) Proceedings of the Sixth International Conference on Learning Analytics and Knowledge, LAK 2016, pp. 89–98. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2883851.2883893

  8. Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11787006_1

    Chapter  Google Scholar 

  9. Garcia-Molina, H., Ullman, J.D., Widom, J.: Database Systems: The Complete Book, 2nd edn. Pearson International Edition, London (2008)

    Google Scholar 

  10. Gray, J., Reuter, A.: Transaction Processing: Concepts and Techniques. Elsevier, Amsterdam (1992)

    MATH  Google Scholar 

  11. Grolinger, K., Higashino, W.A., Tiwari, A., Capretz, M.A.M.: Data management in cloud environments: NoSQL and NewSQL data stores. J. Cloud Comput. Adv. Syst. Appl. 2(1), 1–24 (2013). https://doi.org/10.1186/2192-113X-2-22

    Article  Google Scholar 

  12. Hildt, E., Laas, K.: Informed consent in digital data management. In: Laas, K., Davis, M., Hildt, E. (eds.) Codes of Ethics and Ethical Guidelines. TILELT, vol. 23, pp. 55–81. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-86201-5_4

    Chapter  Google Scholar 

  13. Jørn Nielsen, H., Hjørland, B.: Curating research data: the potential roles of libraries and information professionals. J. Doc. 70(2), 221–240 (2014). https://doi.org/10.1108/JD-03-2013-0034

    Article  Google Scholar 

  14. Kim, Y., Zhang, P.: Understanding data sharing behaviors of stem researchers: the roles of attitudes, norms, and data repositories. Libr. Inf. Sci. Res. 37(3), 189–200 (2015). https://doi.org/10.1016/j.lisr.2015.04.006

    Article  Google Scholar 

  15. Kimball, R.: Slowly Changing Dimensions. Unlike OLTP Systems, Data Warehouse Systems Cab Track Historical Data. DBMS Online, vol. 9, no. 4 (1996)

    Google Scholar 

  16. Linnemann, V., et al.: Design and implementation of an extensible database management system supporting user defined data types and functions. In: VLDB, pp. 294–305 (1988)

    Google Scholar 

  17. Michener, W.K.: Ten simple rules for creating a good data management plan. PLoS Comput. Biol. 11(10), 1–9 (2015). https://doi.org/10.1371/journal.pcbi.1004525

    Article  Google Scholar 

  18. Motro, A.: An access authorization model for relational databases based on algebraic manipulation of view definitions. In: ICDE Fifth International Conference on Data Engineering, pp. 339–347 (1989). https://doi.org/10.1109/ICDE.1989.47234

  19. Obionwu, V., Broneske, D., Hawlitschek, A., Köppen, V., Saake, G.: SQLValidator - an online student playground to learn SQL. Datenbank-Spektrum (2021)

    Google Scholar 

  20. Pampel, H., et al.: Making research data repositories visible: the re3data.org registry. PLoS ONE 8(11), 1–10 (2013). https://doi.org/10.1371/journal.pone.0078080

  21. Pardo, A., Siemens, G.: Ethical and privacy principles for learning analytics. Br. J. Edu. Technol. 45(3), 438–450 (2014). https://doi.org/10.1111/bjet.12152

    Article  Google Scholar 

  22. Pasquetto, I.V., Randles, B.M., Borgman, C.L.: On the reuse of scientific data. Data Sci. J. (2017)

    Google Scholar 

  23. Pouchard, L.: Revisiting the data lifecycle with big data curation. Int. J. Digit. Curation (2015)

    Google Scholar 

  24. Pyrounakis, G., Nikolaidou, M., Hatzopoulos, M.: Building digital collections using open source digital repository software: a comparative study. Int. J. Digital Libr. Syst. (IJDLS) 4(1), 10–25 (2014). https://doi.org/10.4018/ijdls.2014010102

    Article  Google Scholar 

  25. Rizvi, S., Mendelzon, A., Sudarshan, S., Roy, P.: Extending query rewriting techniques for fine-grained access control. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, SIGMOD 2004, pp. 551–562. Association for Computing Machinery, New York (2004). https://doi.org/10.1145/1007568.1007631

  26. Sanamrad, T., Kossmann, D.: Query log attack on encrypted databases. In: Jonker, W., Petković, M. (eds.) SDM 2013. LNCS, vol. 8425, pp. 95–107. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06811-4_14

    Chapter  Google Scholar 

  27. Scheffel, M., Drachsler, H., Slavi, S., Specht, M.: Quality indicators for learning analytics. Educ. Technol. Soc. 17(4), 117–132 (2014)

    Google Scholar 

  28. Smith, M., et al.: DSpace: an open source dynamic digital repository. D-Lib Mag. 9(1) (2003). https://www.dlib.org/dlib/january03/smith/01smith.html

  29. International Organization for Standardization: Space data and information transfer systems - Open archival information system (OAIS) - Reference model. International Organization for Standardization, Vernier, Geneva, Switzerland, ISO 14721:2012-09 edn. (2012). https://www.iso.org/standard/57284.html

  30. Sweeney, L.: K-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowledge-Based Syst. 10(5), 557–570 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  31. Treloar, A., Klump, J.: Updating the data curation continuum. IJDC 14(1), 87–101 (2019). https://doi.org/10.2218/ijdc.v14i1.643

    Article  Google Scholar 

  32. Viberg, O., Hatakka, M., Bälter, O., Mavroudi, A.: The current landscape of learning analytics in higher education. Comput. Hum. Behav. 89, 98–110 (2018). https://doi.org/10.1016/j.chb.2018.07.027

    Article  Google Scholar 

  33. Wilkinson, M.D., et al.: The fair guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18

    Article  Google Scholar 

Download references

Funding

German Federal Ministry of Education and Research [16DHB 3008].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to David Broneske .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Broneske, D., Wolff, I., Köppen, V., Schäler, M. (2022). Exploiting Views for Collaborative Research Data Management of Structured Data. In: Tseng, YH., Katsurai, M., Nguyen, H.N. (eds) From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries. ICADL 2022. Lecture Notes in Computer Science, vol 13636. Springer, Cham. https://doi.org/10.1007/978-3-031-21756-2_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21756-2_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21755-5

  • Online ISBN: 978-3-031-21756-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics